Originally published in LinkedIn.
With a solid fit-plus-intent strategy in place, your next step is to find the right data to create a working model you will use to identify and score high quality leads and accounts. Let's call this your your customer data stack. You will find that one or two sources alone just don't cover enough data points to reliably predict fit and intent. The options are endless, but your strategy should guide you. Are you aiming for an industry niche audience, or maybe a certain size company? Are there personas within your niche that may be your best fit prospects? Are you deploying an ABM strategy, or a blend of inbound and outbound lead generation? Let's take a look at a typical scenario and explore some of the best known input data options.
Target Account - Mid-Size North American FinTech
Account: Mid-Market SaaS Company, B2B, Financial Sector Focus, U.S. and Canada
Persona: C-Suite through Director Level, Revenue, Compliance, P & L, Board Reporting
Account and Persona Fit
Desired Account Data: Company Name, Location, Industry, SIC, Type of Firm, Annual Revenues, Employees, Public/Private, Leadership, Investors, Market Share, Market Cap, Year Founded, Technology Used, Technology Sold, Technology Budget, Competitors
Desired Persona Data: Name, Job Title, Role, Department, Email, Phone, Location, Years in Business, Years in Position, P & L Responsibility (Y/N), Buying Team Lead (Y/N)
Firmographic/Demographic Data Sources: Clearbit, ZoomInfo, DiscoverOrg, LinkedIn, DNB, Hoovers, Alteryx, Aberdeen, InfoUSA, UpLead, Melissa
Technographic Data Sources: HG Data, Datanyze, G2 Crowd, UpLead
Account and Persona Intent
Desired Account/Persona Intent Data: Ads Clicked/Converted, Emails Opened/Clicked, Visits to Website, Chats Opened, Nature of Chats Opened, Velocity of Visits to Website (Recency + Frequency), Specific Pages Visited, Time on Pages + Bounce Rate, Visits to Competitor Sites, Subscription to Blog/Podcast, Relevant Keyword Searches, Interest Level in Relevant Topic, Velocity of Interest in Relevant Topics, Consumption of relevant content, Publication of Relevant Content, Subscription to Relevant Content Sources, Follows of Relevant Authors, Social Media Likes, Relevant Comments/Questions in Social Media, Changes in Job Status and Responsibility, Events Registered + Attended, Calls/Meetings Scheduled, Calls/Meetings Attended
First Party Intent Data: Google Analytics, Google Search Console, Google Adwords, Facebook Ads, Marketing Automation (e.g. HubSpot, Salesforce, Marketo), Drift, LinkedIn (Sales Navigator), Wordpress, Kissmetrics, SEMRush, Intercom, LeadFeeder, PaveAI
Third Party Intent Data: IntentData.io, Bombora, TechTarget, Leadspace, Aberdeen, Triblio, 6Sense, Demandbase, Everstring, Lattice Engines, Madison Logic
Making Intelligent Choices
Before you dive into building an optimized data stack for your business, it's smart to investigate these (and other) data sources and choose the right ones in each category for your target audience and sales and marketing processes. You'll find that there are overlaps and choices by deliverables, price and ease of integration with your tech stack. In my next post, we'll take a look at a few more scenarios and data stacks that make sense and dive into more details between the types of data deployed, why they're ideal, and how much they may cost.
